Cardiovascular disease remains a leading cause of death in North America and Western Europe, with atherosclerosis being the principal cause. It has long been recognized that cholesterol plays a role in the formation of atherosclerotic plaques. The encouraging news, however, is that there are very effective interventions for those at risk of cardiovascular disease. Accordingly, the central challenge is to identify those who are at risk. Of all the factors that are commonly measured to gauge elevated risk, the single most effective indicator is serum low density lipoprotein (LDL) (“bad”) cholesterol. As good as this test is, however, there is now overwhelming evidence that a better alternative exists, namely serum apolipoprotein-B (apoB).
Cholesterol circulates in the bloodstream within complex, multi-component lipoprotein particles. These are further subdivided into very low density (VLDL), low density (LDL), intermediate density (IDL) and high density lipoproteins (HDL). Each LDL, IDL, and VLDL particle is comprised of a single apolipoprotein B molecule, as well as cholesterol, triglycerides, and cholesterol esters. Cholesterol may be imagined as a passenger on a LDL ship. It is now becoming increasingly more evident that the risk of cardiovascular disease is better represented by a number of “ships” (serum LDL particle concentration) than by a number of “passengers” on them (serum LDL cholesterol concentration). As demonstrated, for example, by Walldius G, et. al. (2004), the apoB/apoA-I ratio is better than the cholesterol ratios to estimate the balance between plasma proatherogenic and antiatherogenic lipoproteins and to predict coronary risk (Clinical Chemistry and Laboratory Medicine. 42:1355-1363, and Sniderman A D (1992). It has therefore been recommended that the measurement of apolipoprotein B should replace the conventional lipid profile in screening for cardiovascular risk. Canadian Journal of Cardiology 8:133-138. ApoB remains associated with the LDL, IDL or VLDL particle from the time of its assembly, through its secretion, and metabolic transformation within the circulation, and ultimately to its catabolism.
Total apoB concentration in serum includes LDL, IDL, VLDL, chylomicrons, and lipoprotein(a) particles, and therefore represents an estimate of the total atherogenic particle count. LDL makes up the vast majority of these (Walldius G, et al. Clin Chem Lab Med 42:1355-1363). ApoB has proven superior to LDL cholesterol in gauging risk of cardiovascular disease. Most importantly, the difference in predictive accuracy is most pronounced (in favour of apoB) for those populations most at risk, e.g. elderly males.
The treatment most often prescribed for people at elevated CAD risk is statin therapy, which has the effect of lowering serum LDL cholesterol levels. Recent clinical evidence has shown that here, again, apoB is a better gauge of treatment efficacy. For patients on statin treatment, apoB proved to be a better predictor of outcome than LDL cholesterol, as demonstrated by, Sniderman A D, et al. (2003), Apolipoproteins versus lipids as indices of coronary risk and as targets for statin treatment (Lancet 361:777-780). This research indicates that if statin therapy were guided by targeting desirable serum apoB levels rather than their LDL cholesterol counterparts, statins would reduce the rate of clinical events significantly below that presently achieved.
The potential demand for apoB testing is clear, either as an adjunct to, or replacement for, conventional lipid testing.
While there are numerous ways that apoB can be detected according to the prior art (e.g. electroimmunoassay, radial immunodiffusion, radioimmunoassay enzyme-linked immuno-sorbent assays, nephelometric assays and turbidimetric assays), each of these methods has their own drawbacks that are known in the art. None of these methods has proven easy and cost effective to automate for mass screening. Some of the drawbacks include the requirement for a large volume of antisera, inherent disadvantages of the use of radioactivity, and matrix effect problems (i.e. problems with interferences caused by other components in the serum).
Applicants have previously obtained U.S. Pat. No. 7,022,527, the entire contents of which are incorporated herein by reference, which teaches that a set of clinically relevant serum analytes may be determined accurately based on mid-infrared spectroscopy of serum, including total cholesterol, HDL cholesterol, triglycerides, and LDL cholesterol. Advantageously, this method does not use antisera, and has proven reliable, and cost effective. Naturally a method that analyses sera optically is relatively unconstrained, as the test can be applied on a sample whenever a spectrometer and processor time are available. In contrast, the above-identified methods require other fluids to control or react with the blood serum. These other fluids have shelf-lives and require procedures that are more time consuming, expensive, and difficult to automate.
IR spectroscopy has been applied previously in research studies of lipoprotein structures. For instance, Scanu et al. have employed IR spectroscopy to examine the thermal behavior of apoB (Scanu et al., 1969, PNAS 62: 171-178). IR has also been used to elucidate the secondary structure of apoIB, first qualitatively using resolution-enhancement techniques (Herzyk et al., 1987, Biochim Biophys Acta 922: 145-154) and then quantitatively using curve fitting of deconvolved spectra (Goormaghtigh et al., 1989, Biochim Biophys Acta 1006: 147-150). More recently, Goormaghtigh et al have utilized IR spectroscopy to reveal the structure of the lipid attached proteins that remain following proteolytic digestion of solvent-exposed regions (Goormaghtigh et al., 1993, Biochemistry 32: 6104-6110).
It has been demonstrated previously that IR spectroscopy may be used to quantify several serum analytes, including glucose, urea, triglycerides, total protein, albumin, total cholesterol, LDL cholesterol, and HDL cholesterol. Some of these compounds occur in the highest concentrations in human serum. Indeed, a relatively high concentration and a distinctive infrared absorption spectrum, are both prerequisite to quantification by infrared spectroscopy. The root-mean-square (RMS) error of analyte quantification is typically about 0.1 g/L for the IR spectroscopy-based assays in comparison with accepted clinical analytical counterparts.
Nonetheless, IR spectroscopy cannot be reliably used to detect many serum analytes, for example, because they are present in such low concentrations that they do not contribute meaningfully to the infrared spectrum, or because the mid-infrared absorption pattern closely resembles that of one or more components of similar or higher concentration. An example of the first would be any analyte for which absorptions lie below the noise level of the measurement; so since the RMS error is typically 0.1 g/L for target analytes with favorable spectroscopic absorption profiles, this clearly precludes the meaningful quantification of any analyte (such as serum creatinine and uric acid) with typical concentrations below 0.1 g/L, as RMS error would be larger than the measured concentration. An example of the latter occurs when existing spectroscopic features of the analyte are masked by the absorptions of one or more other components. Under this condition, the RMS error for the target assay would be expected to rise above the benchmark level of 0.1 g/L.
The list of known analytes that cannot be assayed by IR spectroscopy is a long one. It will be appreciated by those skilled in the art that blood serum is an extraordinarily complex mixture, and carries many different components (blood proteins, inorganic electrolytes, glucose, lipids, amino acids, hormones, metabolic end products, carbon dioxide, oxygen, etc.) in concentrations that vary from species to species, from individual to individual, and over time within an individual, for example depending on the health of the individual. As described above, in order to reliably quantify a serum analyte for a target group of individuals, that analyte must provide a set of identifying spectral features that are strong enough to contribute to the measured serum spectrum and are not interfered with by the other components. Because these characteristics may not be known a priori, it is not always known a priori whether a specific serum analyte can be reliably quantified by IR spectroscopy.
The core apolipoproteins within high density and low density lipoprotein particles, apoA-I and apoB, constitute examples of target serum analytes that would not be expected to be amenable to infrared spectroscopy-based quantitative analysis. Both of these compounds typically fall in the concentration range 0.5-1.5 g/L.
The applicants have not found techniques, for example, to produce a test for apoA-I concentration that is satisfactorily reliable, despite the fact that the concentration range for this protein is typically 0.5-1.5 g/L. Although this concentration range lies above the 0.1 g/L uncertainty level typical of glucose, urea, and cholesterol, the attempt to develop an IR-based assay failed. This failure may be caused by the absorption profile of apoA-I being dwarfed by the strong absorptions of serum albumin and globulins, which constitute the major circulating serum proteins. At typical total concentrations of about 70 g/L, the absorption patterns arising from these proteins are 50-70 times stronger than the absorptions contributed by apoA-I. Because the structural elements of proteins are very similar for different proteins, their absorption profiles may only be distinguished under extraordinary circumstances. The expectation, borne out by the experience in attempting the apoA-I assay, is that the absorptions of proteins in blood serum will be masked by the much stronger, superimposed absorption profiles of serum proteins present in larger concentrations (albumin and globulins), and that the overall similarities among these profiles will work in concert to preclude the development of IR-based assays for other proteins.
A reliable, low cost blood serum test for apoB is highly desirable, especially one that could be readily automated.